While traditional optimization problems were often studied in isolation, many real-world problems today require interdependence among multiple optimization components. The traveling thief problem (TTP) is a multi-component problem that has been widely studied in the literature. In this paper, we introduce and investigate the TTP with time window constraints which provides a TTP variant highly relevant to real-world situations where good can only be collected at given time intervals. We examine adaptions of existing approaches for TTP and the Traveling Salesperson Problem (TSP) with time windows to this new problem and evaluate their performance. Furthermore, we provide a new heuristic approach for the TTP with time windows. To evaluate algorithms for TTP with time windows, we introduce new TTP benchmark instances with time windows based on TTP instances existing in the literature. Our experimental investigations evaluate the different approaches and show that the newly designed algorithm outperforms the other approaches on a wide range of benchmark instances.

翻译：传统优化问题往往被孤立研究，而当今许多实际问题要求多个优化组件间的相互依赖关系。旅行窃贼问题（TTP）是一种被广泛研究的多元组合问题。本文提出并研究了带时间窗约束的TTP变体，该变体高度贴合现实中物品只能在特定时间区间内收集的实际场景。我们考察了现有针对TTP和带时间窗旅行商问题（TSP）的方法在本题中的适应性，并评估其性能。此外，我们提出了一种针对带时间窗TTP的新型启发式方法。为评估带时间窗TTP的算法，基于文献中已有的TTP实例，我们引入了新的含时间窗的TTP基准实例。实验研究评估了不同方法，结果表明新设计的算法在广泛的基准实例上优于其他方法。